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DOI:10.2151/jmsj.2015-028
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Reply to the comments of F. Fujibe on "Anthropogenic Heat
Release: Estimation of Global Distribution and Possible Climate
Effect" by Chen B. et al.
Bing Chen1,2, Jian-Qi Zhao2, Liang-Fu Chen 1*, and Guang-Yu Shi2
1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing
and Digital Earth, Chinese Academy of Sciences, Beijing 100101，China
2. The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and
Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing 100029, China
Key Words: Anthropogenic Heat Release(AHR), DMSP/OLS data, global
distribution, climate change
*Corresponding to: Prof. Chen L., [email protected]
1
Modern human society is based on vast consumption of energy resources. All the
energy resources (such as fossil energy, nuclear energy, solar energy, and wind energy)
consumed will eventually turn into anthropogenic heat release(AHR) into the
atmosphere, breaking the energy balance of the Earth-atmosphere system. The
previous research on climate change focused on climatic effect of greenhouse gases
and aerosols, while the anthropogenic heat produced in the energy consumption
process is ignored generally. Chen et al. (2014; hereafter C14) exploring possible
climatic effect of AHR by application of Defense Meteorological Satellite Program’s
Operational Linescan System (DMSP/OLS) data.
The distribution of AHR is difficult to measure accurately for regional scale, even
more difficult for larger scale regions. The application of DMSP/OLS data to estimate
global grid distribution of AHR can solve the difficulty in estimating large-scale
high-precision grid distribution of AHR for climate models. Flanner (2009) estimated
global distribution of AHR based on the population density, and showed that the flux
of AHR is 8.1 W m-2 in Tokyo, and is 48 W m-2 in New York at 0.5°×0.5°resolution.
Fujibe (2014; hereafter F14) commented that the C14’s result of AHR estimation in
2009 is unrealistically large. F14 also stated that regionally-averaged heat fluxes are
sufficiently large (≈1 W m-2) in western Europe and eastern USA, according to
Flanner(2009). From the statistics in 2009 by British Petroleum (2014; hereafter BP),
the mean AHR fluxes are 1.12 W m-2 for UK, 1.15 W m-2 for Germany, 0.74 W m-2
for Italy, 1.68 W m-2 for Japan, 0.31 W m-2 for USA, 0.29 W m-2 for China, and 3.19
W m-2 for South-Korea. Actually, global land mean AHR flux is about 0.10 Wm-2. The
actual scale for " regionally-averaged " should be quantified. Because in the
concentrated regions, the AHR flux is much larger than average level. From the
statistics
by
National
Bureau
of
Statistics
of
China
( http://www.stats.gov.cn/tjsj/ndsj/ ), the annual mean AHR fluxes in 2008 are more
than 4 W m-2 for Beijing, more than 5 W m-2 for Tianjin, more than 16 W m-2 for
Shanghai, and more than 100 W m-2 for Macau (Chen et al. 2011). The AHR is
geographically concentrated and distributed, fundamentally correlating to the
economical activities. The AHR concentrated in the economically developed areas is
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much larger than an average value, reaching a large enough value that could affect
regional climate; while in deserted areas, the flux of AHR is probably zero. In daytime
of central Tokyo, the AHR flux typically exceeds 400 W m-2 with a maximum of 1590
W m-2 in winter (Ichinose et al. 1999). But in a larger scale, the mean flux of AHR
may not seem so large. The scale in the grid of AHR is therefore very important for
the research: the maximum value is larger for a smaller grid in a particular region. The
results of global distribution of AHR in C14 with the grid resolution of 0.1°×0.1°
seem a little different from Flanner's results (Flanner 2009), while they could be
consistent with energy consumption statistics.
F14's comment on C14 focuses on " AHR should be estimated to be constant as
long as "Sum of Lights (SOL)" is unchanged. We can therefore conclude that the
rapid increase of AHR described by C14 is unreliable ". F14's comment insists that if
the SOL is unchanged, the AHR should be the same. The SOL is the total sum of
lights of a country (http:// ngdc. noaa.gov /eog/dmsp/download _national_ trend.html),
this means that the AHR could be changed if the SOL is unchanged; in some regions
AHR is increased, while in some regions AHR is decreased, but the SOL could be the
same. The detailed analysis of the estimating results of AHR by applying DMSP/OLS
data are as follows.
The DMSP/OLS data are closely correlated with economic development levels and
energy consumption. Generally speaking, the areas where the value of DMSP/OLS
data is large are often developed and crowded regions, with more energy consumption
and anthropogenic heat emissions consequently(Chen et al. 2012; Chen and Shi 2012).
In C14, the global grid distribution of AHR from 1992 to 2009 were estimated by
using DMSP/OLS data and applied it in a global climate model to study the global
climatic effect of AHR. The energy consumption statistics of all the countries
provided by BP and the
stable light data from the DMSP/OLS Nighttime Lights
version 4 Time Series (http://ngdc. noaa.gov/eog/ dmsp/download V4composites.html)
were applied to the research. For the analysis presented in this work, continuous
stable light data from 1992 to 2012 from DMSP satellites including the following six
satellites: F10(1992–1994), F12(1994–1999), F14(1997–2003), F15(2000–2007),
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F16(2004–2009) and F18(2010-2012).
The AHR flux Qa in various regions could be obtained by the equation
Qa 
energy consumption[J]
. The mean AHR flux for all the main countries
time[s]  area[m 2 ]
including South Korea, Italy, Germany, UK, Japan, France, Spain, USA, India, and
China from 1992 to 2012 was obtained from the statistics of energy consumption by
BP. The data of
"National trends with intercalibrated DMSP stable light"
(http://ngdc.noaa.gov/eog/dmsp/download_national_trend.html),
the
sum
of
DMSP/OLS data for a nation, provide annual records of SOL in each country since
1992. Considering the possible difference in the sensors of different satellites, the
relationship between the mean AHR flux and the mean SOL were analyzed separately.
The correlation coefficient(R) produced by the linear regression between the stable
light data from 6 satellites and the AHR flux data are listed in Table 1.
Table 1. Linear correlation between the AHR flux and the mean SOL of the main
countries (including South Korea, Italy, Germany, UK, Japan, France, Spain, USA,
India, and China) by 6 satellites from 1992 to 2012.
Satellite
R
F10
0.86
F12
0.85
F14
0.83
F15
0.81
F16
0.79
F18
0.72
Just as Table 1 shows, closely positive correlation is found between AHR and mean
SOL. The results indicate that the DMSP/OLS data is quite closely correlated with
AHR flux. That means that the regions where the DMSP/OLS data are large are
probably populated and developed regions areas, with more energy consumption and
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anthropogenic heat.
The global mean AHR statistically obtained from BP (BP, 2014) and the fitting
results by DMSP/OLS data from 1992 to 2012 are shown in Fig.1. The fitting results
are almost consistent with the global mean AHR, and the correlation coefficient is
0.81. From Fig.1, we can see that since the results of DMSP/OLS data is almost
consistent with global energy consumption, the results of energy consumption
statistics and fitting results are almost the same. The error in the estimating results by
DMSP/OLS data is generally within 12% .
Fig.1 The global mean AHR statistically obtained from BP (in Black) and estimated
global mean AHR from DMSP/OLS (in red) between 1992 and 2012.
All the results suggest that since the DMSP/OLS data is almost consistent with
global energy consumption and AHR, the results seem quite credible upon the global
scale.
Figure 2 shows the discrepancy of the global distribution of AHR between the year
2009(Fig.3 in C14) and 2000 (Fig.2 in C14) by DMSP/OLS data.
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Fig.2
Discrepancy of global distribution of AHR between 2009 and 2000 by
DMSP/OLS data (resolution: 0.1°×0.1°，unit: W m-2).
From Fig.2, the AHR in Europe, South Asian, South America, and East Asia
increases generally between 2000 and 2009. The increasing trend is more obvious in
East China, India and European countries, while the decreasing trend is found in the
regions including Central and Eastern part of USA, East Russia, and Central India.
Additionally, the changing trend seems not significant in some countries such as USA,
South Korea, Japan, Germany, and Spain. The AHR increased in some regions of
these countries, while it decreased in some other regions. That is the probable reason
the SOL in some countries seems changed a little. That is consistent with the fact that
the energy consumption in these regions was found to have changed a little. And
yet there is no denying that some mistake may occur in the Fig.3 of C14; the
increasing trend should not be shown so obviously. The possible cause for this is that
some mistakes occur in displaying colors with the same color bar in Figs. 2 and 3 of
C14. It may be the main comment that F14 focused on.
Due to the high-precision grid and unique ability to detect low levels of visible and
near-infrared radiance at night, the DMSP/OLS data provides an effective way to
estimate large-scale and high-resolution distribution of AHR. Above all, the results of
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global distribution of AHR by applying DMSP/OLS data is generally consistent with
energy consumption statistics, while errors occur inevitably. This method provide us a
new way to obtain credible large-scale continuous high-precision grid distribution of
AHR, which is very important for the research of AHR in climate models.
Acknowledgement
This work was supported by China Postdoctoral Science Foundation (Grant
NO.2013M541077);the Key Program of the National Natural Science Foundation of
China (Grant No. 41130528); National Natural Science Foundation of China (Grant
No. 4150050215, 41305004 and 41271542); The Chinese Academy of Sciences
Strategic
Prior
Research
Program
(Grant
NO.
XDA05040204);
The National Basic Research Development Program of China(Grant No.2011CB9520
01); and Open Project for 2014 of the State Key Laboratory of Numerical Modeling
for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of
Atmospheric Physics, Chinese Academy of Sciences. The mean SOL for all the
countries was obtained using by Arc GIS.
Reference
British Petroleum, 2014: British Petroleum BP Statistical Review of World Energy
2014. [Available at http://www.bp.com/statisticalreview.]
Chen B., G.Y. Shi, T. Dai, Y. Shen, B. Wang, S. Yang, and J.Q. Zhao, 2011: Climate
forcing
due to anthropogenic heat release in China, Climatic and Environmental
Research(in Chinese), 16(6), 717-722.
Chen B., G.Y. Shi, B. Wang, J. Zhao, and S. Tan, 2012: Estimation of the
anthropogenic heat release distribution in China from 1992 to 2009. Acta Meteor.
Sinica, 26(4), 507–515.
7
Chen, B., and G.Y. Shi, 2012: Estimation of the distribution of global anthropogenic
heat flx. Atmos. Oceanic Sci. Lett., 5, 108–112.
Chen, B., L. Dong, G.-Y. Shi, L.-J. Li, and L.-F. Chen, 2014: Anthropogenic heat
release: Estimation of global distribution and possible climate effect. J. Meteor. Soc.
Japan, 92A,157-165.
Flanner, M. G., 2009: Integrating anthropogenic heat flux with global climate models.
Geophys. Res. Lett., 36, L02801, doi:10.1029/2008GL036465.
Fujibe, F., 2014: Comments on "Anthropogenic Heat Release: Estimation of Global
Distribution and Possible Climate Effect" by Chen B. et al., J. Meteor. Soc. Japan, in
press.
Ichinose, T., K. Shimodozono, and K. Hanaki, 1999: Impact of anthropogenic heat on
urban climate in Tokyo. Atmos. Environ., 33, 3897–3909.
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